Get the in-depth framework — plus stories from leaders at companies like DigitalOcean, Moz, AdEspresso, Typeform, and more — to help you convert more of your product qualified leads into happy, paying customers.
Want to know the minute a new chapter is published? Sign up now to get new chapters delivered straight to your inbox!
Creating segmented, relevant marketing content is hard. We struggle to make sense of our data, our tools, our content, and our customers’ voices -- so we continue blasting out the same generic onboarding emails to thousands or millions of very different users.
If your mission is to increase trial > paid conversions, it’s essential to collect and analyze customer data -- which you’ll use to properly segment your user base, craft appropriate messaging for each segment, and personalize communication.
By regularly collecting and reviewing customer data, you can uncover:
1 Trends in how people use your product - the quantitative stuff
2 Trends in why people use your product - the qualitative stuff
When you identify the trends in how and why people use your product, you’ll have a clear view of the ideal experience your highest-value prospects need to go through to convert to paying customers.
The goal is to be able to qualify leads and send different messages based on different actions a user takes.
The Customer Stack model shows how data flows together between messaging potential customers, tracking their reactions, recording to a profile, deciding actions to message those potential customers again.
1. Choose a data integration method to tie your product usage data to your CRM. customer stack together (loop between sending messages, tracking reactions, recording to a profile, deciding actions to sending more messages.
It’s important to find the specific triggers along the path to your user’s “Aha!” moment.
Gut instinct is good enough to get started with basic segmentation. But gut instinct is NOT good enough for triggering personalized messages. You need to dig into your data to understand what those unique triggers are for your product.
There are three common techniques to this:
1. Raw product usage (quick start, easy. Less than 15 minutes)
2. Quick regression analysis (identify more and better opportunities. An hour or less)
3. Data science and artificial intelligence. Find many triggers, and deliver precise content (ongoing, five-figure spend)
1. Identify Your “aha!” moments with raw product usage, quick regression analysis and data science. Start small.
2. Pull your raw product usage and graphically determine what features appear to result in a jump in spend. This isn’t science, but it’s a start. You can do this yourself quickly.
3. Next, find correlations with quick regression analysis. This may take an hour of your time looking over several combinations of product usage data. Again, you can do this yourself without significant budget.
4. Finally, when you’re sure you have the volume and complexity, look into building a predictive model with data scientists. This will take time to train and refine.
Segments group alike contacts together so you can then trigger logic in other tools, like sales tasks, email workflows, ad targeting, live chat and more.
Segmentation allows you to codify the rules and triggers you've found to be significant for use in all your tools.
1. Define your segmentation plan according to the four key dimensions for B2B product qualified leads: lifecycle stage, individual person’s profiles, company profiles (“firmographics”) and the key actions you identified earlier.
2. Sync your segments to all your tools, so you can manage messaging across multiple channels to your target accounts.
You know what trigger events a trial user in any particular segment needs to reach to increase the odds of an upgrade.
The problem is, that trial user doesn’t care about “trigger events.” She cares about solving a problem she currently faces.
So to effectively guider her toward each trigger event, you need to get out of the office, and into her head. You’ve got to start thinking about what actually motivates her, and writing like she actually speaks.
1. Identify moments along the customer journey where qualitative input could be collected (directly after signup, in an onboarding email, in-app message, chat, in a re-engagement email -- if user isn’t activating as desired -- directly after upgrade)
2. Confirm a user’s responses are being recorded to their profile, so responses can be analyzed per segment → patterns identified
3. Conduct 1:1 interviews with ~10 customers in each top-priority segment to uncover the deeper motivations shaping their buying journey
Once you’re collecting qualitative customer data, you’ll have tons of material with which to write high-converting messages across your ads, your website, and throughout communication with your users during their trial experience.
To leverage that data, sort the key details into top pains, motivations, and stickiest words / phrasing that appear within each segment -- as outlined in this VOC worksheet.
This shows you which messages resonate most strongly with this segment -- so you can craft content that addresses users’ most important needs first, and guides them to “Aha” much more efficiently.
1. Complete the VOC worksheet using the data you collected in Section 6
The campaign you’ll create for each segment should act as a trail of breadcrumbs, guiding users toward each trigger action they need to take to reach “Aha” -- and, ultimately, to upgrade.
Remember those old slide projectors, where you could layer clear sheets of plastic on top of one another to create a more and more complete image? That’s what you’re going to do with your quantitative data (profile, firmographics, event actions) and VOC data to convert people from trial > paid.
- Your segment is the bottom slide: it’s who you’re talking to
- Your event actions are the middle slide: it’s what you’re guiding them to do
- Your VOC data is the top slide: it’s HOW you talk to this group of users
1. From the VOC worksheet you completed, pull that segment’s top…
a. Pains pushing them to seek a new solution in the first place (before they found you)
b. Improvements / better life they envision once they find a solution
c. Words & phrases used to describe ^^ and your product
d. Interesting stories of people’s journeys from trial > paid (any noteworthy fears / doubts they had about the product early on? Any cool anecdotes around product usage)
2. Based on how many actions a user must take to reach “Aha,” plot out the number of messages to send (use the Message Map above as a guide
3. Decide which channels you’ll use, and which content content format fits best (emails that look like sales/marketing? Help docs? Case studies? Blog posts?)
4. Write your messages
5. Launch your campaign
6. Test and measure
Leveraging product usage data is a cross-functional challenge. You need to be able to bring together product, engineering, sales, and marketing around one core objective.
1. Identify a strategy for how to communicate to each team and bring alignment - use the examples in Chapter 10.
2. Identify how you can use product usage data to support each team’s own initiatives beyond just qualifying leads for sales. (e.g. account management for customer success, lifecycle nurturing for marketing).
Claire Suellentrop helps high-growth SaaS companies get inside their customers’ heads. Previously the Director of Marketing and #2 employee at Calendly, she’s seen firsthand that truly effective marketing stems from a deep understanding of existing users’ needs.
Now, she works with companies like Wistia, FullStory, and MeetEdgar to uncover their best customers’ needs and desires, then uses those juicy details to create more relevant, high-converting marketing and onboarding campaigns.
Head of Growth, Hull.io
Ed Fry is passionate about helping marketers grow their organizations and directly contribute to revenue. He was the first employee at Inbound.org and worked with thought-leaders like Rand Fishkin, Co-Founder of Moz, and Dharmesh Shah, Co-Founder of HubSpot. During his tenure, membership grew from 5,000 to 165,000+ members between 2012 and 2016.
Ed currently oversees Growth at Hull - a customer data management platform that eliminates data problems for marketing and sales teams alike.
Don't want to miss out on the upcoming chapters? Sign up now and we'll deliver each chapter as soon as we publish!
No spam, no gimmicks.
Just great content.